R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)

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+ ,0
+ ,17
+ ,16
+ ,64
+ ,39
+ ,19540
+ ,6095
+ ,35873
+ ,27
+ ,21
+ ,15)
+ ,dim=c(16
+ ,173)
+ ,dimnames=list(c('pageviews'
+ ,'time_in_rfc'
+ ,'logins'
+ ,'compendium_views_info'
+ ,'compendium_views_pr'
+ ,'shared_compendiums'
+ ,'blogged_computations'
+ ,'compendiums_reviewed'
+ ,'feedback_messages_p1'
+ ,'feedback_messages_p120'
+ ,'totsize'
+ ,'totrevisions'
+ ,'totseconds'
+ ,'tothyperlinks'
+ ,'totblogs'
+ ,'I1')
+ ,1:173))
>  y <- array(NA,dim=c(16,173),dimnames=list(c('pageviews','time_in_rfc','logins','compendium_views_info','compendium_views_pr','shared_compendiums','blogged_computations','compendiums_reviewed','feedback_messages_p1','feedback_messages_p120','totsize','totrevisions','totseconds','tothyperlinks','totblogs','I1'),1:173))
>  for (i in 1:dim(x)[1])
+  {
+  	for (j in 1:dim(x)[2])
+  	{
+  		y[i,j] <- as.numeric(x[i,j])
+  	}
+  }
> par4 = 'yes'
> par3 = '2'
> par2 = 'none'
> par1 = '10'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Dr. Ian E. Holliday
> #To cite this work: Ian E. Holliday, 2009, YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: 
> #Technical description: 
> library(party)
Loading required package: survival
Loading required package: splines
Loading required package: grid
Loading required package: modeltools
Loading required package: stats4
Loading required package: coin
Loading required package: mvtnorm
Loading required package: zoo
Loading required package: sandwich
Loading required package: strucchange
Loading required package: vcd
Loading required package: MASS
Loading required package: colorspace
> library(Hmisc)

Attaching package: 'Hmisc'

The following object(s) are masked from 'package:survival':

    untangle.specials

The following object(s) are masked from 'package:base':

    format.pval, round.POSIXt, trunc.POSIXt, units

> par1 <- as.numeric(par1)
> par3 <- as.numeric(par3)
> x <- data.frame(t(y))
> is.data.frame(x)
[1] TRUE
> x <- x[!is.na(x[,par1]),]
> k <- length(x[1,])
> n <- length(x[,1])
> colnames(x)[par1]
[1] "feedback_messages_p120"
> x[,par1]
  [1]  94 103  93  91  93  60 123  90 168 115  71  66 117 108  84 120 114  94
 [19] 120  81 133 122 124 126   0  37  38 120  95  77  90  80 110 138 100   7
 [37] 140  96 164  78  49 124  62  99  70 104 116  91  67  72 120 105 104  98
 [55] 111  71  69 107  73 107 129 118  73 119 104 107  90 197  36  85 139 106
 [73]  50  63  63  69  41  56  25  93  44  87 110   0  83  80  98  82   0  60
 [91]  28   9  33  59 115 120  66 152 139  38 144 160 114 119 101  56 133  83
[109] 116  50  61  97  98  78 117  55 132  44  21  50  73  86  48  48  68  87
[127]  43  67  46  56  60  65  60  54  52  61  61  81  40  40  68  79  47  41
[145]  29  60  79  47  40  42  49  57  40  33  77  45  45  50  71  67  62  54
[163]   4  25  40  59  24  58  42   4  63  54  39
> if (par2 == 'kmeans') {
+ cl <- kmeans(x[,par1], par3)
+ print(cl)
+ clm <- matrix(cbind(cl$centers,1:par3),ncol=2)
+ clm <- clm[sort.list(clm[,1]),]
+ for (i in 1:par3) {
+ cl$cluster[cl$cluster==clm[i,2]] <- paste('C',i,sep='')
+ }
+ cl$cluster <- as.factor(cl$cluster)
+ print(cl$cluster)
+ x[,par1] <- cl$cluster
+ }
> if (par2 == 'quantiles') {
+ x[,par1] <- cut2(x[,par1],g=par3)
+ }
> if (par2 == 'hclust') {
+ hc <- hclust(dist(x[,par1])^2, 'cen')
+ print(hc)
+ memb <- cutree(hc, k = par3)
+ dum <- c(mean(x[memb==1,par1]))
+ for (i in 2:par3) {
+ dum <- c(dum, mean(x[memb==i,par1]))
+ }
+ hcm <- matrix(cbind(dum,1:par3),ncol=2)
+ hcm <- hcm[sort.list(hcm[,1]),]
+ for (i in 1:par3) {
+ memb[memb==hcm[i,2]] <- paste('C',i,sep='')
+ }
+ memb <- as.factor(memb)
+ print(memb)
+ x[,par1] <- memb
+ }
> if (par2=='equal') {
+ ed <- cut(as.numeric(x[,par1]),par3,labels=paste('C',1:par3,sep=''))
+ x[,par1] <- as.factor(ed)
+ }
> table(x[,par1])

  0   4   7   9  21  24  25  28  29  33  36  37  38  39  40  41  42  43  44  45 
  3   2   1   1   1   1   2   1   1   2   1   1   2   1   5   2   2   1   2   2 
 46  47  48  49  50  52  54  55  56  57  58  59  60  61  62  63  65  66  67  68 
  1   2   2   2   4   1   3   1   3   1   1   2   5   3   2   3   1   2   3   2 
 69  70  71  72  73  77  78  79  80  81  82  83  84  85  86  87  90  91  93  94 
  2   1   3   1   3   2   2   2   2   2   1   2   1   1   1   2   3   2   3   2 
 95  96  97  98  99 100 101 103 104 105 106 107 108 110 111 114 115 116 117 118 
  1   1   1   3   1   1   1   1   3   1   1   3   1   2   1   2   2   2   2   1 
119 120 122 123 124 126 129 132 133 138 139 140 144 152 160 164 168 197 
  2   5   1   1   2   1   1   1   2   1   2   1   1   1   1   1   1   1 
> colnames(x)
 [1] "pageviews"              "time_in_rfc"            "logins"                
 [4] "compendium_views_info"  "compendium_views_pr"    "shared_compendiums"    
 [7] "blogged_computations"   "compendiums_reviewed"   "feedback_messages_p1"  
[10] "feedback_messages_p120" "totsize"                "totrevisions"          
[13] "totseconds"             "tothyperlinks"          "totblogs"              
[16] "I1"                    
> colnames(x)[par1]
[1] "feedback_messages_p120"
> x[,par1]
  [1]  94 103  93  91  93  60 123  90 168 115  71  66 117 108  84 120 114  94
 [19] 120  81 133 122 124 126   0  37  38 120  95  77  90  80 110 138 100   7
 [37] 140  96 164  78  49 124  62  99  70 104 116  91  67  72 120 105 104  98
 [55] 111  71  69 107  73 107 129 118  73 119 104 107  90 197  36  85 139 106
 [73]  50  63  63  69  41  56  25  93  44  87 110   0  83  80  98  82   0  60
 [91]  28   9  33  59 115 120  66 152 139  38 144 160 114 119 101  56 133  83
[109] 116  50  61  97  98  78 117  55 132  44  21  50  73  86  48  48  68  87
[127]  43  67  46  56  60  65  60  54  52  61  61  81  40  40  68  79  47  41
[145]  29  60  79  47  40  42  49  57  40  33  77  45  45  50  71  67  62  54
[163]   4  25  40  59  24  58  42   4  63  54  39
> if (par2 == 'none') {
+ m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
+ }
> 
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
> 
> if (par2 != 'none') {
+ m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x)
+ if (par4=='yes') {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'',1,TRUE)
+ a<-table.element(a,'Prediction (training)',par3+1,TRUE)
+ a<-table.element(a,'Prediction (testing)',par3+1,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Actual',1,TRUE)
+ for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
+ a<-table.element(a,'CV',1,TRUE)
+ for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
+ a<-table.element(a,'CV',1,TRUE)
+ a<-table.row.end(a)
+ for (i in 1:10) {
+ ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1))
+ m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,])
+ if (i==1) {
+ m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,])
+ m.ct.i.actu <- x[ind==1,par1]
+ m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,])
+ m.ct.x.actu <- x[ind==2,par1]
+ } else {
+ m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,]))
+ m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1])
+ m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,]))
+ m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1])
+ }
+ }
+ print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred))
+ numer <- 0
+ for (i in 1:par3) {
+ print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,]))
+ numer <- numer + m.ct.i.tab[i,i]
+ }
+ print(m.ct.i.cp <- numer / sum(m.ct.i.tab))
+ print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred))
+ numer <- 0
+ for (i in 1:par3) {
+ print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,]))
+ numer <- numer + m.ct.x.tab[i,i]
+ }
+ print(m.ct.x.cp <- numer / sum(m.ct.x.tab))
+ for (i in 1:par3) {
+ a<-table.row.start(a)
+ a<-table.element(a,paste('C',i,sep=''),1,TRUE)
+ for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj])
+ a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4))
+ for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj])
+ a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4))
+ a<-table.row.end(a)
+ }
+ a<-table.row.start(a)
+ a<-table.element(a,'Overall',1,TRUE)
+ for (jjj in 1:par3) a<-table.element(a,'-')
+ a<-table.element(a,round(m.ct.i.cp,4))
+ for (jjj in 1:par3) a<-table.element(a,'-')
+ a<-table.element(a,round(m.ct.x.cp,4))
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/191en1323893906.tab") 
+ }
+ }
> m

	 Conditional inference tree with 9 terminal nodes

Response:  feedback_messages_p120 
Inputs:  pageviews, time_in_rfc, logins, compendium_views_info, compendium_views_pr, shared_compendiums, blogged_computations, compendiums_reviewed, feedback_messages_p1, totsize, totrevisions, totseconds, tothyperlinks, totblogs, I1 
Number of observations:  173 

1) feedback_messages_p1 <= 93; criterion = 1, statistic = 152.105
  2) feedback_messages_p1 <= 41; criterion = 1, statistic = 75.856
    3)*  weights = 12 
  2) feedback_messages_p1 > 41
    4) feedback_messages_p1 <= 78; criterion = 1, statistic = 49.205
      5) feedback_messages_p1 <= 58; criterion = 1, statistic = 17.978
        6)*  weights = 14 
      5) feedback_messages_p1 > 58
        7)*  weights = 41 
    4) feedback_messages_p1 > 78
      8)*  weights = 25 
1) feedback_messages_p1 > 93
  9) feedback_messages_p1 <= 123; criterion = 1, statistic = 49.105
    10) totsize <= 55183; criterion = 0.96, statistic = 8.986
      11)*  weights = 7 
    10) totsize > 55183
      12)*  weights = 34 
  9) feedback_messages_p1 > 123
    13) compendiums_reviewed <= 38; criterion = 1, statistic = 21.844
      14) totrevisions <= 16714; criterion = 0.964, statistic = 9.176
        15)*  weights = 8 
      14) totrevisions > 16714
        16)*  weights = 18 
    13) compendiums_reviewed > 38
      17)*  weights = 14 
> postscript(file="/var/wessaorg/rcomp/tmp/2aggy1323893906.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m)
> dev.off()
null device 
          1 
> postscript(file="/var/wessaorg/rcomp/tmp/3pz8t1323893906.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response')
> dev.off()
null device 
          1 
> if (par2 == 'none') {
+ forec <- predict(m)
+ result <- as.data.frame(cbind(x[,par1],forec,x[,par1]-forec))
+ colnames(result) <- c('Actuals','Forecasts','Residuals')
+ print(result)
+ }
    Actuals Forecasts    Residuals
1        94  96.05882  -2.05882353
2       103  96.05882   6.94117647
3        93 103.12500 -10.12500000
4        91  96.05882  -5.05882353
5        93  96.05882  -3.05882353
6        60  72.88000 -12.88000000
7       123 120.61111   2.38888889
8        90  96.05882  -6.05882353
9       168 144.42857  23.57142857
10      115  96.05882  18.94117647
11       71  96.05882 -25.05882353
12       66  72.88000  -6.88000000
13      117 120.61111  -3.61111111
14      108  96.05882  11.94117647
15       84  72.88000  11.12000000
16      120 120.61111  -0.61111111
17      114  96.05882  17.94117647
18       94  96.05882  -2.05882353
19      120 120.61111  -0.61111111
20       81  70.28571  10.71428571
21      133 120.61111  12.38888889
22      122 144.42857 -22.42857143
23      124 120.61111   3.38888889
24      126 120.61111   5.38888889
25        0  12.25000 -12.25000000
26       37  40.21429  -3.21428571
27       38  40.21429  -2.21428571
28      120 120.61111  -0.61111111
29       95 103.12500  -8.12500000
30       77  72.88000   4.12000000
31       90  96.05882  -6.05882353
32       80  70.28571   9.71428571
33      110 103.12500   6.87500000
34      138 144.42857  -6.42857143
35      100  96.05882   3.94117647
36        7  12.25000  -5.25000000
37      140 144.42857  -4.42857143
38       96  96.05882  -0.05882353
39      164 144.42857  19.57142857
40       78  72.88000   5.12000000
41       49  53.07317  -4.07317073
42      124 120.61111   3.38888889
43       62  72.88000 -10.88000000
44       99  96.05882   2.94117647
45       70  70.28571  -0.28571429
46      104  96.05882   7.94117647
47      116 120.61111  -4.61111111
48       91  96.05882  -5.05882353
49       67  72.88000  -5.88000000
50       72  96.05882 -24.05882353
51      120 120.61111  -0.61111111
52      105  96.05882   8.94117647
53      104 103.12500   0.87500000
54       98  96.05882   1.94117647
55      111 120.61111  -9.61111111
56       71  72.88000  -1.88000000
57       69  72.88000  -3.88000000
58      107  96.05882  10.94117647
59       73  53.07317  19.92682927
60      107  96.05882  10.94117647
61      129 120.61111   8.38888889
62      118 120.61111  -2.61111111
63       73  72.88000   0.12000000
64      119 103.12500  15.87500000
65      104  96.05882   7.94117647
66      107  96.05882  10.94117647
67       90  96.05882  -6.05882353
68      197 144.42857  52.57142857
69       36  53.07317 -17.07317073
70       85  72.88000  12.12000000
71      139 144.42857  -5.42857143
72      106 120.61111 -14.61111111
73       50  70.28571 -20.28571429
74       63  96.05882 -33.05882353
75       63  53.07317   9.92682927
76       69  72.88000  -3.88000000
77       41  40.21429   0.78571429
78       56  53.07317   2.92682927
79       25  12.25000  12.75000000
80       93  96.05882  -3.05882353
81       44  40.21429   3.78571429
82       87  72.88000  14.12000000
83      110 144.42857 -34.42857143
84        0  12.25000 -12.25000000
85       83  96.05882 -13.05882353
86       80  70.28571   9.71428571
87       98 103.12500  -5.12500000
88       82  96.05882 -14.05882353
89        0  12.25000 -12.25000000
90       60  72.88000 -12.88000000
91       28  12.25000  15.75000000
92        9  12.25000  -3.25000000
93       33  40.21429  -7.21428571
94       59  53.07317   5.92682927
95      115 120.61111  -5.61111111
96      120 103.12500  16.87500000
97       66  72.88000  -6.88000000
98      152 144.42857   7.57142857
99      139 144.42857  -5.42857143
100      38  40.21429  -2.21428571
101     144 144.42857  -0.42857143
102     160 144.42857  15.57142857
103     114  96.05882  17.94117647
104     119  96.05882  22.94117647
105     101  96.05882   4.94117647
106      56  53.07317   2.92682927
107     133 120.61111  12.38888889
108      83  70.28571  12.71428571
109     116 120.61111  -4.61111111
110      50  53.07317  -3.07317073
111      61  53.07317   7.92682927
112      97  96.05882   0.94117647
113      98  96.05882   1.94117647
114      78  72.88000   5.12000000
115     117 144.42857 -27.42857143
116      55  53.07317   1.92682927
117     132 144.42857 -12.42857143
118      44  40.21429   3.78571429
119      21  12.25000   8.75000000
120      50  53.07317  -3.07317073
121      73  96.05882 -23.05882353
122      86 103.12500 -17.12500000
123      48  70.28571 -22.28571429
124      48  53.07317  -5.07317073
125      68  72.88000  -4.88000000
126      87  72.88000  14.12000000
127      43  40.21429   2.78571429
128      67  53.07317  13.92682927
129      46  40.21429   5.78571429
130      56  53.07317   2.92682927
131      60  53.07317   6.92682927
132      65  53.07317  11.92682927
133      60  53.07317   6.92682927
134      54  53.07317   0.92682927
135      52  53.07317  -1.07317073
136      61  53.07317   7.92682927
137      61  72.88000 -11.88000000
138      81  72.88000   8.12000000
139      40  40.21429  -0.21428571
140      40  53.07317 -13.07317073
141      68  53.07317  14.92682927
142      79  72.88000   6.12000000
143      47  53.07317  -6.07317073
144      41  53.07317 -12.07317073
145      29  40.21429 -11.21428571
146      60  53.07317   6.92682927
147      79  72.88000   6.12000000
148      47  53.07317  -6.07317073
149      40  40.21429  -0.21428571
150      42  53.07317 -11.07317073
151      49  53.07317  -4.07317073
152      57  53.07317   3.92682927
153      40  53.07317 -13.07317073
154      33  53.07317 -20.07317073
155      77  72.88000   4.12000000
156      45  40.21429   4.78571429
157      45  40.21429   4.78571429
158      50  53.07317  -3.07317073
159      71  72.88000  -1.88000000
160      67  72.88000  -5.88000000
161      62  53.07317   8.92682927
162      54  53.07317   0.92682927
163       4  12.25000  -8.25000000
164      25  12.25000  12.75000000
165      40  53.07317 -13.07317073
166      59  53.07317   5.92682927
167      24  12.25000  11.75000000
168      58  53.07317   4.92682927
169      42  53.07317 -11.07317073
170       4  12.25000  -8.25000000
171      63  53.07317   9.92682927
172      54  53.07317   0.92682927
173      39  53.07317 -14.07317073
> if (par2 != 'none') {
+ print(cbind(as.factor(x[,par1]),predict(m)))
+ myt <- table(as.factor(x[,par1]),predict(m))
+ print(myt)
+ }
> postscript(file="/var/wessaorg/rcomp/tmp/483621323893906.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> if(par2=='none') {
+ op <- par(mfrow=c(2,2))
+ plot(density(result$Actuals),main='Kernel Density Plot of Actuals')
+ plot(density(result$Residuals),main='Kernel Density Plot of Residuals')
+ plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals')
+ plot(density(result$Forecasts),main='Kernel Density Plot of Predictions')
+ par(op)
+ }
> if(par2!='none') {
+ plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted')
+ }
> dev.off()
null device 
          1 
> if (par2 == 'none') {
+ detcoef <- cor(result$Forecasts,result$Actuals)
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goodness of Fit',2,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Correlation',1,TRUE)
+ a<-table.element(a,round(detcoef,4))
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'R-squared',1,TRUE)
+ a<-table.element(a,round(detcoef*detcoef,4))
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'RMSE',1,TRUE)
+ a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4))
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/5e9e81323893906.tab") 
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'#',header=TRUE)
+ a<-table.element(a,'Actuals',header=TRUE)
+ a<-table.element(a,'Forecasts',header=TRUE)
+ a<-table.element(a,'Residuals',header=TRUE)
+ a<-table.row.end(a)
+ for (i in 1:length(result$Actuals)) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,result$Actuals[i])
+ a<-table.element(a,result$Forecasts[i])
+ a<-table.element(a,result$Residuals[i])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/6gdpp1323893906.tab") 
+ }
> if (par2 != 'none') {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'',1,TRUE)
+ for (i in 1:par3) {
+ a<-table.element(a,paste('C',i,sep=''),1,TRUE)
+ }
+ a<-table.row.end(a)
+ for (i in 1:par3) {
+ a<-table.row.start(a)
+ a<-table.element(a,paste('C',i,sep=''),1,TRUE)
+ for (j in 1:par3) {
+ a<-table.element(a,myt[i,j])
+ }
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/7d7l51323893906.tab") 
+ }
> 
> try(system("convert tmp/2aggy1323893906.ps tmp/2aggy1323893906.png",intern=TRUE))
character(0)
> try(system("convert tmp/3pz8t1323893906.ps tmp/3pz8t1323893906.png",intern=TRUE))
character(0)
> try(system("convert tmp/483621323893906.ps tmp/483621323893906.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  5.224   0.275   5.525